Electro-Hydro-Dynamic-Force-Driven Filling Method for Through Polymer Substrates Via With Ag-Based Conductive Epoxy
Bibliographic record
Abstract
Through polymer substrates via (TPSV) plays an important role in system integration for not only silicon-based devices but also emerging flexible electronics implemented with polymer substrates. Conventional electroplating approach for filling TPSVs encounters high cost and environmental issues such as complex preprocessing, long deposition time, heavy metal ions, and pollutant side-products from the electrolytes. To tackle these challenges, this work presents an electro-hydro-dynamic-force (EHDF) driven filling method for TPSVs with conductive polymers. A customized bench-top setup is implemented to demonstrate the feasibility of the filling method. The proposed EHDF-driven filling technique can fill TPSVs of various aspect ratios (ARs) completely within 1 min, utilizing electrostatic fields. We established an empirical model for the correlation of the driving voltage and filling depth for various substrate thicknesses and TPSVs ARs. The experimental results show that the resistance of filled TPSVs is proportional to their AR. The lowest resistance is about$1~\Omega $for the TPSV with an AR of 1. The conductive property of EHDF-filled TPSVs is only limited by the intrinsic resistivity of the filler material, therefore, demonstrating promising application potentials in emerging devices integration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".